ArticleResearch involvement and engagement2022
Fantastic perspectives and where to find them: involving patients and citizens in digital health research.
Article in Research involvement and engagement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Coconstructing CHAMP, an Artificial Intelligence Chatbot for Pediatric Infectious Symptoms Management: Protocol for a Multiphase Participatory Study.JMIR research protocols · 2026Article
- Perspective on patient and non-academic partner engagement for the responsible integration of large language models in health chatbots.Research involvement and engagement · 2025Article
- Key Components of Participatory Design Workshops for Digital Health Solutions: Nominal Group Technique and Feasibility Study.Journal of healthcare informatics research · 2025Article
- Engaging people with lived experience on community advisory boards in community-based participatory research: a scoping review.International journal for equity in health · 2025Article
- The behavioural and cognitive impacts of digital educational interventions in the emergency department: A systematic review.PLOS digital health · 2025Article
- Using Text Messaging Surveys in General Practice Research to Engage With People From Low-Income Groups: Multi-Methods Study.JMIR mHealth and uHealth · 2024Article
- Adopting a learning pathway approach to patient partnership in telehealth: A proof of concept.PEC innovation · 2023Article
- The language of vaccination campaigns during COVID-19.Medical humanities · 2023Article
- The reception of public health messages during the COVID-19 pandemic.Applied Corpus Linguistics · 2023Article
- Lessons Learned from the Implementation of a Person-Centred Digital Health Platform in Cancer Care.Current oncology (Toronto, Ont.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDigital contact tracing and exposure notification apps have quickly emerged as a potential solution to achieve timely and effective contact tracing for the SARS-CoV-2 virus. Nonetheless, their actual uptake remains limited. Citizens, including patients, are rarely consulted and included in the design and implementation process. Their contribution supports the acceptability of such apps, by providing upstream evidence on incentives and potential barriers that are most relevant to users. The DIGICIT (DIGITal CITizenship) project relied on patient and citizen partnership in research to better integrate public perspectives on these apps. In this paper, we present the co-construction process that led to the survey instrument used in the DIGICIT project and the interpretation of its results. This approach promotes public participation in research on contact tracing and exposure notification apps, as well as related digital health applications.
objectivesThis article has three objectives: (1) describe the methodological process to co-construct a questionnaire and interpret the survey results with patients and citizens, (2) assess their experiences regarding this methodology, and (3) propose best practices for their involvement in digital health research.
methodsThe DIGICIT project was developed in four steps: (1) creation of the advisory committee composed of patients and citizens, (2) co-construction of a questionnaire, (3) interpretation of survey results, and (4) assessment of the experience of committee participants.
resultsOf the 25 applications received for participation in the advisory committee, we selected 12 people based on pre-established diversity criteria. Participants initially generated 84 survey questions in the first co-construction meeting, and eventually selected 36 in the final version. Participants made more than 20 recommendations when interpreting survey results and suggested carrying out focus groups with marginalized populations to increase representativity. They appreciated their inclusion early in the research process, being listened to and respected, the collective intelligence, and the method used for integrating their suggestions. They suggested that the study objectives and roles be better defined, that more time in the brainstorming sessions be allowed, and that discussion outside of meetings be encouraged.
conclusionHaving patients and citizens actively participating in this research constitutes the main methodological strength. They enriched the study from start to finish, and recommended the addition of focus groups to seek the perspective of marginalized groups that are typically under-represented from digital health research. Clear communication of the project objectives, good organization in meetings, and continuous evaluation from participants allow best practices to be achieved for patients' and citizens' involvement in digital health research. Co-construction in research generates critical study design ideas through collective intelligence. This methodology can be used in various clinical contexts and different healthcare settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.